Learn three methods to perform PCA on categorical or mixed data types in Python: one-hot encoding, factor analysis, and mixed data PCA. Compare their advantages and disadvantages.

Principal Component Analysis (PCA) Implementation This repository contains a custom implementation of the Principal Component Analysis (PCA) algorithm in Python. It showcases how PCA can be applied to ...

Learn what is PCA, why use it, and how to perform it in Python or R using scikit-learn and prcomp functions with examples. Agree & Join LinkedIn ...

This repository contains Python classes for handling 2D and 3D datasets using NumPy arrays. It includes methods for loading data, displaying data shapes, plotting data, and applying Principal ...

Tensor robust principal component analysis (PCA) approaches have drawn considerable interests in many applications such as background subtraction, denoising, and outlier detection, etc. In this paper ...

Principal Component Analysis (PCA) has been successfully used for many application including ear recognition. However, its performance is limited due to its significant data dependency. This paper ...